Time series decomposition in R
Time series analysis is one of those scary sounding terms that in reality is very simple. All it means is that you have data where the independent variable is time (seconds, minutes, hours, days, weeks, months, or years) and a dependent that changes over time. Time series analysis is just methods for detecting trends in the dependent variable over time. The most basic time series analysis is linear regression, which I previously covered here . In this post, I'll discuss time series decomposition. Time series decomposition means that you break a time series into its constituent parts: Trend, seasonal, and random. Seasonal means changes that occur in a regular cycle over the course of a year. Random is random fluctuations within a time series that are neither part of the seasonal pattern nor the trend. I'll demonstrate time series decomposition using Antarctic sea ice data . First, a graph of monthly average Antarctic sea ice since satellite...